Enterprises have accumulated knowledge about their customers, business practices, and financials within electronic data stores. A variety of software services or systems permit the enterprises to populate, query, mine, and manage their knowledge included within their data stores.
One problem associated with these services and systems is that there appears to be no centralized service or mechanism for managing access to and from the data stores in such a manner that knowledge can be reused and leveraged in an automated fashion within the enterprise.
As an example, consider an enterprise that queries and mines a data store for insurance fraud. If one fraud investigator develops a useful query for detecting a particular fraud situation; that query is often not easily communicated to other fraud investigators within the enterprise and is often not easily modified to a specific set of circumstances that the other fraud investigators may be faced with for their fraud scenarios. As a result, the knowledge that the original fraud investigator created for accessing the data store is lost or if not lost, effectively not usable or not usable in an efficient manner. That is, if subsequent fraud investigators are forced to request a database administrator to modify a search query for their particular scenarios then at least some benefits associated with the query are lost since additional time and resources are needed to use the original query.
Another problem with conventional data store access techniques is that existing queries are not easily enhanced or extended. Because queries are often hard coded and stored locally within a user's local environment, the ability to leverage certain aspects of the queries and extend them in an automated fashion is lost.
Thus, it can be seen that queries are not efficiently generated, populated, and managed within enterprise environments.